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Updated: Feb 4, 2026

Preparation of Complaint Matrices for Quantifying Cellular Contraction

Preparation of Complaint Matrices for Quantifying Cellular Contraction

Published on: December 14, 2010

Computationally derived compound profiling matrices.

Martin Vogt¹, Swarit Jasial¹, Jürgen Bajorath¹

¹Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53113 Bonn, Germany.

Future Science OA

|October 2, 2018

Summary

This summary is machine-generated.

Researchers created three compound screening profiling matrices from public data. These matrices aid in analyzing and predicting ligand-target interactions, crucial for drug discovery.

Keywords:

biological screening compound profiling matrices computational design open access data targets test compounds

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Last Updated: Feb 4, 2026

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Area of Science:

Computational chemistry
Cheminformatics
Drug discovery

Background:

Compound screening against target panels generates profiling matrices.
These matrices are valuable for analyzing and predicting ligand-target interactions.
Publicly available screening data can be leveraged to create these matrices.

Discussion:

A novel algorithm was developed to generate comprehensive profiling matrices from assay data.
These matrices serve as benchmarks for evaluating computational chemogenomics tools.
The matrices facilitate the assessment of predictive models for ligand-target relationships.

Key Insights:

Three profiling matrices, derived from both primary and confirmatory assays, are now publicly accessible.
The matrices encompass a significant number of targets and compounds, offering robust datasets.
These resources enable rigorous testing and validation of computational methods in drug discovery.

Outlook:

Further profiling matrices will be generated, focusing on specific target families.
Continued development and application of computational chemogenomics are anticipated.
These datasets will aid in advancing the prediction of drug-target interactions.